An analysis of EEG signal power spectrum density generated during writing in children with dyslexia

Mahmoodin, Z.; Mansor, W.; Lee, Khuan Y.; Mohamad, N. B.

doi:10.1109/cspa.2015.7225637

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…The frequency domain feature extraction techniques have been used to capture EEG signal characteristics which may be relevant to evaluate cognitive workload. The Power Spectral Density (PSD) feature is generally used for feature extraction in the frequency domain [1,13,20] [21]. They also proposed that for a brain enhancement system, the brain connectome approach is very helpful.…”

Section: Related Workmentioning

confidence: 99%

“…The removal of the artifacts is essential for further processing. Power spectral density (PSD) is one of the potential feature extraction techniques to differentiate the variations in electrophysiological processing of the brain [13]. Artifact-free EEG signal is segmented into epochs and power spectra are computed by the use of FFT [14,15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Cognitive Workload Identification using EEG Power Spectrum

Anshul,

Bansal,

Mahajan

2019

IJRTE

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Now a days, Electroencephalography (EEG) is popular to monitor human’s cognitive workload. EEG signals are delicate to the variation in cognitive load in various fields including observing cognitive workload for the intricate environment of military chores. Earlier to acquire the EEG signals high-cost EEG systems were used which bounds their use but now a day’s low-cost headsets are available to capture EEG which makes it a promising set-up to measure cognitive workload. EEGs are initially preprocessed to reflect the artifacts present in it. After preprocessing, signals are ready for further processing. The power spectral density corresponds to the power distribution of EEG signal in the frequency domain which is used to assess the changes in the pattern of the brain. This paper discusses the present progress of research in cognitive workload identification and identifies the techniques associated with the cognitive workload. This proposed research gives the analysis of EEG signal power spectrum density (PSD) during resting state and cognitive workload activities of a human. With power spectral analysis of the EEG signal, seven statistical parameters have been calculated (minimum, maximum, mean, median, mode, standard deviation and range) have been calculated Analysis showed that the in cognitive workload, PSD has significantly changed if compared to the resting state

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Cognitive Workload Identification using EEG Power Spectrum

Anshul,

Bansal,

Mahajan

2019

IJRTE

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“…The EEG signals collected then classified in to delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) bands the following table lists the frequencies Table 1. EEG Bands and Description [5] Gibson's test is used for testing brain skills like cognitive, motor skills, math, memory abilities, And EEG is used in understanding brain process and related functions and "Power spectral density" is used to extract features to recognize differences in brain EEG processing in kids with dyslexia [6] Seven key areas are considered for the matrices they are. 80 children (40 boys and 40 girls)records of 7 to 13 age, were considered, which was analysed and proposed a computerized analytical model which included computing system, with dataset that differentiates into "non dyslexic(Normal )", or "Dyslexic", or ADHD or inattention etc".…”

Section: Introductionmentioning

confidence: 99%

Review: Mass Screening framework for children with dyslexia using IOT and computing analysis

Mulakaluri¹,

Girisha²

2020

EAI Endorsed Transactions on Internet of Things

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Dyslexia is a medical disorder due to which children have difficulties in learning and reproducing the learnt concepts. In this context children are mostly considered to be not interested or negligent towards their studies. According to dyslexia association of India 10-15 % of children enrolled in schools suffer from some type of Dyslexia. Awareness on Learning difficulties and detection is a complicated. As detection of LD requires diverse features it requires proper guidance and intervention. The issues with explicit learning difficulties in kids have been a reason for worry to parents and educators. Its challenging for the teachers and educationalists to differentiate students with LD and other students. This paper mainly analysis of the paper titled "Diagnosis of Dyslexia using Computing Analysis", Electroencephalogram (EEG) as a tool is used for understanding of brain process and related functions, Number of factors related to Dyslexia and "Power spectral density" is extracted using Gibson test for brain skills. And to identify differences in brain processing using EEG Technology in kids with dyslexia and non-dyslexic. Data sets are generated classifying Dyslexic and non dyslexic and were analysed using the K-means, Fuzzy and ANN classifiers. then the results obtained from these classifiers differentiate between the three different groups (dyslexic, non-dyslexic and disordered). And the next paper "Spatial Blockchain-based Secure Mass Screening Framework for Children with Dyslexia", this paper was mainly focused in detection of symptoms of dyslexia at an age of 8-11 years, so that children can be assisted with various assisting tools and take technologic support so that children can take part in regular schools. This paper is based on Mobile Edge Computing, cloud computing.

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“…In previous studies, some features extracted from EEG signal to find distinguishable feature during writing were power spectrum [12], frequency content [13] and DWT [14]. These features were employed in machine learning with a promising result such as in K-nearest neighbour (KNN) [15] and Support Vector Machine (SVM) [16].…”

Section: Introductionmentioning

confidence: 99%